An algorithm that is configured to perform well on artificial function suites might not perform well in the context of neural network training. This could be due to the unbounded search spaces of neural network weights, but also because the characteristics of the search landscapes differ from traditional function optimisation. The proposed CORNN Suite complements existing benchmark suites by providing an additional large set of neural network training tasks that can easily be used to benchmark the performance of any continuous black-box algorithm.
In general, desirable features of an optimisation benchmarking suite include the following [1]}:
